Gait recognition aims at identifying people by their walking postures. To solve the problem of poor matching between the Effective Receptive Field （ERF） and the human silhouette region， a gait recognition method based on atrous convolution， named DilatedGait， was proposed. Firstly， atrous convolution was employed to expand the neurons’ receptive fields， thereby alleviating the resolution degradation by downsampling and model deepening. Therefore， the recognizability of the silhouette structure was enhanced. Secondly， Dilated Reparameterization Module （DRM） was proposed to optimize the ERF focus range by fusing the multi-scale convolution kernel parameters through reparameterization method， thus enabling the model to capture more global contextual information. Finally， the discriminative gait features were extracted via feature mapping. Experiments were conducted on the outdoor datasets Gait3D and GREW， and the results show that compared with the existing state-of-the-art method GaitBase， DilatedGait improves 9.0 and 14.2 percentage points respectively in Rank-1 and mean Inverse Negative Penalty （mINP） on Gait3D and increases 11.6 and 8.8 percentage points respectively in Rank-1 and Rank-5 on GREW. It can be seen that DilatedGait overcomes the adverse effects of complex covariates and further enhances the accuracy of gait recognition in outdoor scenes.

Recommending developers for open-source projects is of great significance to the construction of open-source ecology. Different from traditional software development， developers， projects， organizations and correlations in the open-source field reflect the characteristics of open collaborative projects， and their embedded semantics help to recommend developers accurately for open-source projects. Therefore， a Developer Recommendation method based on Collaborative Contribution Network （DRCCN） was proposed. Firstly， a CCN was constructed by utilizing the contribution relationships among Open-Source Software （OSS） developers， OSS projects and OSS organizations. Then， based on CCN， a three-layer deep heterogeneous GraphSAGE （Graph SAmple and aggreGatE） Graph Neural Network （GNN） model was constructed to predict the links between developer nodes and open-source project nodes， so as to generate the corresponding embedding pairs. Finally， according to the prediction results， the K-Nearest Neighbor （KNN） algorithm was adopted to complete the developer recommendation. The proposed model was trained and tested on GitHub dataset， and the experimental results show that compared to the contrastive learning model for sequential recommendation CL4SRec （Contrastive Learning for Sequential Recommendation）， DRCCN improves the precision， recall， and F1 score by approximately 10.7%， 2.6%， and 4.2%， respectively. It can be seen that the proposed model can provide important reference for the developer recommendation of open-source community projects.

As the development of artificial intelligence， deep neural network has become an essential tool in various pattern recognition tasks. Deploying deep Convolutional Neural Networks （CNN） on edge computing equipment is challenging due to storage space and computing resource constraints. Therefore， deep network compression has become an important research topic in recent years. Low-rank decomposition and vector quantization are the most popular network compression techniques， which both try to find a compact representation of the original network， thereby reducing the redundancy of network parameters. By establishing a joint compression framework， a deep network compression method based on low-rank decomposition and vector decomposition — Quantized Tensor Decomposition （QTD） was proposed to obtain higher compression ratio by performing further quantization based on the low-rank structure of network. Experimental results of classical ResNet and the proposed method on CIFAR-10 dataset show that the volume can be compressed to 1% by QTD with a slight accuracy drop of 1.71 percentage points. Moreover， the proposed method was compared with the quantization-based method PQF （Permute， Quantize， and Fine-tune）， the low-rank decomposition-based method TDNR （Tucker Decomposition with Nonlinear Response）， and the pruning-based method CLIP-Q （Compression Learning by In-parallel Pruning-Quantization） on large dataset ImageNet. Experimental results show that QTD can maintain better classification accuracy with same compression range.

The commonly used monocular vision-based vehicle 3D detection method at present combines object detection with geometric constraint. However， the position of the vanishing point in the geometric constraint has a significant impact on the results. To obtain more accurate constraint conditions， a 3D vehicle detection algorithm based on horizon line detection was proposed. First， the relative position of the vanishing point was obtained using the vehicle image， and the vehicle image was preprocessed to an appropriate size. Then， the preprocessed vehicle image was fed into a vanishing point detection network to obtain a set of heatmaps indicating the vanishing point information. The vanishing point information was regressed， and the horizon information was calculated. Finally， geometric constraint was constructed based on the horizon line information， and the initial dimensions of the vehicle were iteratively optimized within the constrained space to calculate the precise 3D information of the vehicle. The experimental results demonstrate that the proposed horizon line solving algorithm obtains more accurate horizon lines. Compared to the random forest method， there is an AUC （Area Under Curve） improvement of 1.730 percentage points. Simultaneously， the introduced horizon line constraint effectively restricts the 3D vehicle information， resulting in an average precision improvement of 2.201 percentage points compared to the algorithm using diagonal and vanishing point constraint. It can be observed that the horizon line serves as a geometric constraint for solving vehicle 3D information in the context of roadside monocular camera perspectives.

With the rapid development and wide application of the Internet of Things （IoT） and cloud storage technology， an increasing number of sensor devices are deployed to the Internet of Medical Things （IoMT） system every year， which promotes the popularization of Electronic Health Record （EHR）. However， the secure storage and retrieval of EHRs have not been properly resolved. To address this problem， a searchable attribute-based encryption scheme with a fixed-length trapdoor was constructed for the search and verification of ciphertext， which reduced the communication overhead required by users. By adopting the online/offline encryption technology， the computing overhead on the user side was reduced. Meanwhile， with the help of chameleon hash function， a private key with the characteristics of anti-collision and semantical security was constructed， which avoided the problem of frequent updating of private keys of unrevoked users and greatly reduced the computing overhead of users. Theoretical analysis and experimental results show that the proposed scheme can resist chosen-plaintext attack under the Decisional Bilinear Diffie-Hellman （DBDH） assumption， and compared with other similar attribute based encryption schemes， the proposed scheme is more efficient， which supports online encryption， efficient user revocation， and has lower computational and storage overheads.

A multi-scale dilated convolution based Unmanned Aerial Vehicle （UAV） image target detection algorithm Swin-Det was proposed to address the issues of complex target scenes， diverse scales of targets， dense small targets and severe occlusion of targets in UAV aerial images. Firstly， Swin Transformer was used as the backbone feature extraction network， and a Spatial Information Blending Module （SIBM） was introduced into the backbone network to solve the problem of fuzziness in target information due to occlusion between objects. Secondly， a Fusion of Dilation Feature Pyramid Network （FDFPN） was proposed to fuse feature information through multi-branch dilated convolution， thereby effectively improving the receptive field of the network and the reuse of feature information， so that the model was able to learn detailed features of different dimensions. Finally， the issues of mismatches in the prediction area and sample imbalance were addressed by using linear interpolation method and multi-task loss function， thereby improving the detection precision of the model. Experimental results on VisDrone dataset show that the Swin-Det algorithm reaches a mean Average Precision （mAP） of 27.2%， which is 4.1 percentage points higher than that of the original Swin Transformer， and converges faster under the same training batch. It can be seen tha the Swin-Det algorithm can achieve high-precision detection of UAV image targets in complex scenes.

Federated Learning （FL） allows users to share and interact with multiple parties without directly uploading the original data， effectively reducing the risk of privacy leaks. However， existing research suggests that the adversary can still reconstruct raw data through shared gradient information. To further protect the privacy of federated learning， a deep shadow defense scheme of federated learning based on Generative Adversarial Network （GAN） was proposed. The original real data distribution features were learned by GAN and replaceable shadow data was generated. Then， the original model trained on real data was replaced by a shadow model trained on shadow data and was not directly accessible to the adversary. Finally， the real gradient was replaced by the shadow gradient generated by the shadow data in the shadow model and was not accessible to the adversary. Experiments were conducted on CIFAR10 and CIFAR100 datasets for comparison of the proposed scheme with the five defense schemes of adding noise， gradient clipping， gradient compression， representation perturbation and local regularization and sparsification. On CIFAR10 dataset， the Mean Square Error （MSE） and the Feature Mean Square Error （FMSE） of the proposed scheme were 1.18-5.34 and 4.46-1.03×10⁷ times， and the Peak Signal-to-Noise Ratio （PSNR） of the proposed scheme was 49.9%-90.8%. On CIFAR100 dataset， the MSE and the FMSE of the proposed scheme were 1.04-1.06 and 5.93-4.24×10³ times， and the PSNR of the proposed scheme was 96.0%-97.6%. Compared with the deep shadow defense method， the proposed scheme takes into account the actual attack capability of the adversary and the problems in shadow model training， and designs threat models and shadow model generation algorithms. It performs better in theory analysis and experiment result that of the comparsion schemes， and it can effectively reduce the risk of federated learning privacy leaks while ensuring accuracy.

Concerning the dynamic change of link delay， clock timing interference， and uncertainty of timestamp acquisition caused by complex link environment and temperature fluctuation in Industrial Wireless Sensor Networks （IWSNs）， a time synchronization method based on Precision Time Protocol （PTP） in IWSNs was proposed. Firstly， the clock state space model and observation model were constructed by integrating the clock timing interference and asymmetric link delay noise in PTP bidirectional time synchronization process. Secondly， a reverse adaptive Kalman filter algorithm was constructed to remove the noise interference. Thirdly， the rationality of the noise statistical model was evaluated by using the clock state normalized innovation ratio of the reverse estimation and the forward estimation. Finally， the process noise of the clock state was dynamically adjusted after setting the detection threshold， thereby achieving precise estimation of clock parameters. Simulation results show that compared with the classical Kalman filter algorithm and PTP protocol， the proposed algorithm has the clock offset and skew estimation with smaller and more stable error standard deviations under different clock timing precision. The reverse adaptive Kalman filter can effectively solve the problem of Kalman filter divergence caused by reasons such as noise uncertainty， and improve the reliability of time synchronization.

Improving the image quality of IntraVascular Optical Coherence Tomography （IVOCT） through guidewire artifact removal can assist physicians in diagnosing cardiovascular diseases more accurately， which reduces the probabilities of misdiagnosis and missed diagnosis. Aiming at the difficulties of complex structure information and a large proportion of artifact areas in IVOCT images， a Structure-Enhanced Transformer Network （SETN） using Generative Adversarial Network （GAN） architecture was proposed for guidewire artifact removal of IVOCT images. Firstly， based on the ORiginal Image （ORI） backbone generation network for extracting texture features， the generator of GAN was combined with RTV （Relative Total Variation） image enhanced generation network in parallel to obtain image structure information. Next， during the artifact area reconstruction of ORI/RTV image， Transformer encoders focusing on the temporal/spatial domain information respectively were introduced to capture the contextual information and the correlation between texture/structure features of IVOCT image sequence. Finally， the structural feature fusion module was used to integrate the structural features of different levels into the decoding stage of the ORI backbone generation network， so that the generator was cooperated with the discriminator for completing the image reconstruction of the guidewire artifact area. Experimental results show that the guidewire artifact removal results of SETN are excellent in both texture and structure reconstruction. Besides， the improvement of IVOCT image quality after guidewire artifact removal is positive for both vulnerable plaque segmentation and lumen contour extraction tasks of IVOCT image.

Aiming at the problems of a lot of specialized vocabulary， sparse features， and a large degree of label confusion in pre-hospital emergency text， a Label Confusion Model （LCM）-based text classification model was proposed. Firstly， Bidirectional Encoder Representation from Transformers （BERT） was used to obtain dynamic word vectors and fully exploit semantic information of specialized vocabulary. Then， the text representation vector was generated by fusing Bidirectional Long Short-Term Memory （BiLSTM） network， weighted convolution， and attention mechanism to improve the feature extraction capability of the model. Finally， LCM was used to obtain semantic associations between text and labels， and dependencies between labels to solve the problem of a large degree of label confusion. In the experiments conducted on the pre-hospital emergency text and public news text datasets， the F1 scores of the LCM-based text classification model reached 93.46% and 97.08%， respectively， which were 0.95% to 7.01% and 0.38% to 2.00% higher than those of the models such as Text Convolutional Neural Network （TextCNN）， BiLSTM， and BiLSTM-Attention， respectively. Experimental results show that the proposed model can obtain the semantic information of specialized vocabulary， extract text features more accurately， and effectively solve the problem of large degree of label confusion. At the same time， the proposed model has a certain generalization ability.

Aiming at the problem that the existing microblog event extraction methods are based on the content characteristics of events and ignore the relationship between the social attributes and time characteristics of events， so that they cannot identify the key events in the propagation process of microblog hot spots， a key event extraction method from microblog by integrating social influence and temporal distribution was proposed. Firstly， the social influence was modeled to present importance of microblog events. Secondly， the temporal characteristics of microblog events during evolution were considered to capture the differences of events under different temporal distributions. Finally， the key microblog events were extracted under different temporal distributions. Experimental results on real datasets show that the proposed method can effectively extract key events in microblog hot spots. Compared with four methods of random selection， Term Frequency-Inverse Document Frequency （TF-IDF）， minimum-weight connected dominating set and degree and clustering coefficient information， the proposed method has the event set integrity index improved by 21%， 18%， 26% and 30% on dataset 1 respectively， and 14%， 2%， 21% and 23% on dataset 2 respectively. The extraction effect of the proposed method is better than those of the traditional methods.

Aiming at the problem of insufficient acquisition of document semantic information in the field of science and technology，a set of rule-based methods for extracting semantics from domain-dependent mathematical text were proposed. Firstly， domain concepts were extracted from the text and semantic mapping between mathematical entities and domain concepts were realized. Secondly， through context analysis for mathematical symbols， entity mentions or corresponding text descriptions of mathematical symbols were obtained and the semantics of the symbols were extracted. Finally， the semantic analysis of expressions was completed based on the extracted semantics of mathematical symbols. Taking linear algebra texts as research examples， a semantic tagging dataset was constructed for experiments. Experimental results show that the proposed methods achieve a precision higher than 93% and a recall higher than 91% on semantic extraction of identifiers， linear algebra entities and expressions.

Software testing is an effective way to ensure the quality of Android applications. Understanding the functions of Android applications is the basis of the Android testing process. It aims to deeply explore the application’s business logic and reveal its functional defects， playing an important role in testing. User behavior patterns can assist testers in understanding an Android application’s functions， thereby improving test efficiency. Based on the idea “similar Android applications share user behavior patterns”， a user behavior pattern mining and reuse method was proposed to reduce the cost of Android application testing and improve the testing efficiency. Specifically， for the Android application under test， the user behavior patterns from a similar Android application were mined. Then， the semantic-based event fuzzy matching strategy was used to search the corresponding events for the application under test， and the Graphical User Interface （GUI） model based optimal path selection strategy was used to generate target event sequences for the application under test， thereby achieving user behavior pattern reuse across similar applications. The experiments were conducted on 32 user behavior patterns of three categories of Android applications. The results show that 87.4% of user behavior patterns can be completely reused on similar Android applications， and the reused user behavior patterns can effectively cover 90.2% of important states in applications under test. Thus， the proposed method provides effective support for the testing of Android applications.

Existing materialized view asynchronous incremental maintenance task generation algorithms under Hybrid Transaction/Analytical Processing （HTAP） are mainly used for multiple records and unable to generate materialized view asynchronous incremental maintenance task under HTAP for single record， which results in the increase of disk IO overhead and the performance degradation of materialized view asynchronous incremental maintenance under HTAP. Therefore， a materialized view asynchronous incremental maintenance task generation method under HTAP for single record was proposed. Firstly， the benefit model of materialized view asynchronous incremental maintenance task generation under HTAP for single record was established. Then， the materialized view asynchronous incremental maintenance task generation under HTAP for single record algorithm was designed on the basis of Q-learning. Experimental results show that materialized view asynchronous incremental maintenance task generation under HTAP for single record is realized by the proposed algorithm， and the proposed algorithm decreases the average IOPS （Input/output Operations Per Second）， average CPU utilization （2-core） and average CPU utilization （4-core） at least by 8.49 times， 1.85 percentage points and 0.97 percentage points respectively.

Federated Learning （FL） is a novel privacy?preserving learning paradigm that can keep users' data locally. With the progress of the research on FL， the shortcomings of FL， such as single point of failure and lack of credibility， are gradually gaining attention. In recent years， the blockchain technology originated from Bitcoin has achieved rapid development， which pioneers the construction of decentralized trust and provides a new possibility for the development of FL. The existing research works on blockchain?based FL were reviewed， the frameworks for blockchain?based FL were compared and analyzed. Then， key points of FL solved by the combination of blockchain and FL were discussed. Finally， the application prospects of blockchain?based FL were presented in various fields， such as Internet of Things （IoT）， Industrial Internet of Things （IIoT）， Internet of Vehicles （IoV） and medical services.

Aiming at the problem of spectrum allocation based on maximizing network benefit in cognitive radio and the fact that Manta Ray Foraging Optimization （MRFO） algorithm is difficult to solve the problem of spectrum allocation， a Discrete Manta Ray Foraging Optimization （DMRFO） algorithm was proposed.Considering the pro-1 characteristic of spectrum allocation problem in engineering， firstly， MRFO algorithm was discretely binarized based on the Sigmoid Function （SF） discrete method. Secondly， the XOR operator and velocity adjustment factor were used to guide the manta rays to adaptively adjust the position of next time to the optimal solution according to the current velocity. Then， the binary spiral foraging was carried out near the global optimal solution to avoid the algorithm from falling into the local optimum. Finally， the proposed DMRFO algorithm was applied to solve the spectrum allocation problem. Simulation results show that the convergence mean and standard deviation of the network benefit when using DMRFO algorithm to allocate spectrum are 362.60 and 4.14 respectively， which are significantly better than those of Discrete Artificial Bee Colony （DABC） algorithm， Binary Particle Swarm Optimization （BPSO） algorithm and Improved Binary Particle Swarm Optimization （IBPSO） algorithm.

As a key platform for people to acquire and disseminate news events， Weibo hides rich event information. Extracting storylines from Weibo data provides users with an intuitive way to accurately understand event evolution. However， the data sparseness and lack of context make it difficult to extract storylines from Weibo data. Therefore， two consecutive tasks for extracting storylines automatically from Weibo data were introduced： 1） events were modeled by propagation impact of Weibo， and the primary events were extracted； 2） the heterogeneous event graph was built based on the event features， and an Event Graph Convolution Network （E-GCN） model was proposed to improve the learning ability of implicit relations between events， so as to predict story branches of the events and link the events. The proposed method was evaluated from the perspectives of story branch and storyline on real datasets. In story branch generation evaluation， the results show that compared with Bayesian model， Steiner tree and Story forest， the proposed method has the F1 value higher by 28 percentage points， 20 percentage points and 27 percentage points on Dataset1 respectively， and higher by 19 percentage points， 12 percentage points and 22 percentage points on Dataset2 respectively. In storyline extraction evaluation， the results show that compared with Story timeline， Steiner tree and Story forest， the proposed method has the correct edge accuracy higher by 33 percentage points， 23 percentage points and 17 percentage points on Dataset1 respectively， and higher by 12 percentage points， 3 percentage points and 9 percentage points on Dataset2 respectively.

Due to the increase of system complexity arising from the partial substitution of the traditional 51 Micro Controller Unit (MCU) with Cortex M3, a method was proposed for concurrent control on embedded system without operating system.First of all, a script language and its corresponding interpreter for concurrent control were implemented via context-free grammar formally; further an proposition was pointed out that multithreading was a sufficient but not necessary condition for concurrent scripts; meanwhile, a coroutine mechanism for concurrent programming on embedded system was constructed by switching script contexts in the interrupt service routine of MCU timer. The experimental results show that the proposed mechanism avoids the dependency on commercial multithreading libraries, which is helpful to decrease the cost of product, promotes the readability of source code and causes a flash programming frequency drop by about 58%.The respective applications on systems with and without operating system demonstrate that this mechanism is portable and practical.

Most of the P2P traffic identification methods have the problem of high time cost. Therefore, it was proposed to use TWin Support Vector Machine (TWSVM) whose time cost was a quarter of the common Support Vector Machine (SVM) to build classifier. Kmeans ensemble was used to create labeled sample set and labeled sample set was combined as the training sample of the TWSVM. At last, the constructed classification model was used to identify P2P traffic. The experimental results show that the method based on Kmeans and TWSVM can significantly decrease time cost of the P2P traffic identification, and has a higher accuracy rate and better stability than the standard SVM.

Concerning the recognition of feather quill crease feature, a new feature extraction method was proposed. In order to solve the scaling and translation sensitivity of Radon transform, an improved Radon transform was used to extract moment invariants of target region and local projection technology was adopted to eliminate interference physiological texture of feather quill. Obtaining invariants matrix by changing the scale factor, Singular Value Decomposition (SVD) was provided here to obtain feature invariant for classification and recognition. The results show that this suggested method has higher robustness and higher recognition rate compared with other algorithms.

Since Genetic Programming (GP) is of natural parallelism, the parallel and distributed GP model was developed, including master-slave model, island model and grid model. However, the realizing algorithm of these distributed models is complex and they cannot be reused. It is difficult to achieve the scale computation of GP quickly based on different topologies. Due to these shortcomings, the authors presented the graph-based parallel distributed GP model which realizes formal description of the various operations of GP, and could support the distributed parallel computation of GP for different topologies. It is easy to achieve the master-slave model, island model and grid model of GP through experimental test. The new model is stable, efficient and easy to realize.

In view of the limitation of the traditional Transmission Control Protocol (TCP) used in the heterogeneous network, a network cognitive model based on fuzzy comprehensive evaluation was proposed. This model, by building the membership functions and different dynamic weight distribution under the different network environment, distinguished the wireless error loss from congestion loss according to fuzzy comprehensive evaluation. The simulation results show: compared with the traditional TCP, under different network conditions, the model can more accurately distinguish the wireless error loss from congestion loss, and improve the TCP throughput and network performance.

This paper gave a solution to emergency task in space. When the emergency task occurred, at first, the existing satellite constellation was used to cover the target locations and the coverage rate was computed. If the coverage performance did not meet the mission requirements, the Non-Dominated Sorting Genetic Algorithm Ⅱ （NSGA-Ⅱ） was used to optimize the layout of satellite constellation, i.e. optimizing the anomaly of each satellite in the constellation. Then the phase modulation maneuver was used to achieve constellation optimization results, i.e. maneuvering the satellites to the specified locations, and calculating maneuver time and energy of each satellite. Finally, an emergency task example and its solution process were provided, and the time and energy of the satellite orbit maneuver were calculated.

A modified Normalized cut (Ncut) method considering the texture weight was proposed to effectively segment feather quill. The weight including texture information enhanced association on each edge of similar texture and reduced interference of the similar region. Narrow unidirectional expansion method with a region-scalable fitting term was used to optimize the initial boundary for the final result and eliminated boundary leakage of the feather quill. The experimental results show that the proposed method can realize boundary extraction of feather quill efficiently and pave the way for the next step research of crease detection.

Concerning the high time complexity of inference and parameter estimation in graph model，the concept of superpixel was introduced into the Conditional Random Field (CRF)，and a superpixel-based CRF image classification method was proposed. This method first over segmented the image into small homogeneous regions which were called superpixels by using mean shift method. Then the graphical model was constructed with superpixels as nodes and the neighboring nodes as edges. The corresponding definition of CRF and the methods for parameter estimation and labeling inference were proposed and implemented. The experimental results show that better classification results are obtained by the superpixel-based CRF model. At the same time, running time is largely reduced.

Most of the existing similarity measurement, based on Euclidean distance, cannot be applied directly and effectively to similarity matching of the timeseries with different granularities. This paper proposed a new similarity measure based on the sample of the corresponding D-value. It firstly expounded the definition of the time-series with different granularities, and defined the sample of the corresponding D-value; secondly it put forward the similarity matching algorithm; finally, the experimental results prove that the algorithm can effectively measure the similarity of time-series with multiple granularities.

The algebraic reconstruction technique family, including Algebraic Reconstruction Techniques (ART), Multiplicative Algebraic Reconstruction Techniques (MART), Simultaneous Iterative Reconstruction Techniques (SIRT), was studied to reconstruct the three-dimensional distribution of water vapor in this article. The simulation experiment was carried out on Shanghai GPS network. The results show that the algebraic reconstruction technology family can work effectively in water vapor tomography, get rapid convergence, and implement more easily. The range of relaxation factor and the initial value of iteration were also given in the article.

A motion planning framework for virtual human arm manipulation was proposed based on bidirectional heuristic Rapidly-exploring Random Tree (RRT). According to whether a manipulated object was hold by hand, the framework proceeded in two phases: a reaching phase and a moving phase. Forward as well as inverse kinematic algorithm was used in these two phases respectively in order to make planning result rapid and credible. The effectiveness of our method was demonstrated by experiments.

A QoS unicast routing scheme with ABC supported was proposed. The interval was used to describe the uncertain users' QoS requirement and inaccurate edge (link) parameter. With the user satisfaction degree, edge evaluation and elitist solution set introduced, a QoS unicast path was searched by particle swarm optimization algorithm and gaming analysis, achieving or approaching Pareto optimal solution under Nash equilibrium on both the network provider utility and the user utility along the found path. Simulation results show that the proposed scheme is both feasible and effective.

Fast retrieval and fine granularity access control of massive inhomogeneous documents are key points of document management system towards industry applications. A classification document organizing and retrieval system on B/S mode was designed in this paper. It achieves fast retrieval and role-based multi-level access control of each document.